package com.atguigu.gmall.realtime.app.dws;

import com.atguigu.gmall.realtime.app.func.KeywordUDTF;
import com.atguigu.gmall.realtime.beans.KeywordBean;
import com.atguigu.gmall.realtime.utils.MyClickhouseUtil;
import com.atguigu.gmall.realtime.utils.MyKafkaUtil;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

/**
 * @author Felix
 * @date 2023/2/6
 * 流量域：搜索关键词聚合统计
 * 需要启动的进程
 *      zk、kafka、flume、clickhouse、DwdTrafficBaseLogSplit、DwsTrafficSourceKeywordPageViewWindow
 */
public class DwsTrafficSourceKeywordPageViewWindow {
    public static void main(String[] args) throws Exception {
        //TODO 1.基本环境准备
        //1.1 指定流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //1.2 设置并行度
        env.setParallelism(4);
        //1.3 指定表执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        //1.4 注册自定义的UDTF函数
        tableEnv.createTemporarySystemFunction("ik_analyze", KeywordUDTF.class);

        //TODO 2.检查点相关的设置(略)
        //TODO 3.从kafka的页面日志主题中读取数据 创建动态表并指定Watermark以及提取事件时间字段
        tableEnv.executeSql("CREATE TABLE page_log (\n" +
            "   common map<string,string>,\n" +
            "   page map<string,string>,\n" +
            "   ts bigint,\n" +
            "   row_time as TO_TIMESTAMP(FROM_UNIXTIME(ts/1000)),\n" +
            "   WATERMARK FOR row_time AS row_time - INTERVAL '3' SECOND\n" +
            ")" + MyKafkaUtil.getKafkaDDL("dwd_traffic_page_log","dws_traffic_keyword_group"));
        // tableEnv.executeSql("select * from page_log").print();

        //TODO 4.过滤出搜索行为
        Table searchTable = tableEnv.sqlQuery("select \n" +
            "   page['item'] fullword,\n" +
            "   ts,\n" +
            "   row_time\n" +
            "from page_log\n" +
            "where page['last_page_id']='search' and \n" +
            "page['item_type']='keyword' and page['item'] is not null");
        tableEnv.createTemporaryView("search_table",searchTable);
        // tableEnv.executeSql("select * from search_table").print();

        //TODO 5.对搜索的内容进行分词  并将分词结果和原表字段进行关联
        Table splitTable = tableEnv.sqlQuery("SELECT keyword,row_time FROM search_table,\n" +
            "LATERAL TABLE(ik_analyze(fullword)) t(keyword)");
        tableEnv.createTemporaryView("split_table",splitTable);
        // tableEnv.executeSql("select * from split_table").print();

        //TODO 6.分组、开窗、聚合
        Table reduceTable = tableEnv.sqlQuery("select \n" +
            "    DATE_FORMAT(TUMBLE_START(row_time, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') stt,\n" +
            "    DATE_FORMAT(TUMBLE_END(row_time, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') edt,\n" +
            " keyword,\n" +
            " count(*) keyword_count,\n" +
            " UNIX_TIMESTAMP()*1000 ts\n" +
            "from split_table group by keyword,TUMBLE(row_time, INTERVAL '10' SECOND)");
        // tableEnv.createTemporaryView("reduce_table",reduceTable);
        // tableEnv.executeSql("select * from reduce_table").print();

        //TODO 7.将动态表转换为流
        DataStream<KeywordBean> keywordBeanDS = tableEnv.toAppendStream(reduceTable, KeywordBean.class);
        keywordBeanDS.print(">>>>");

        //TODO 8.将流中数据写到Clickhouse
        keywordBeanDS.addSink(
            MyClickhouseUtil.getSinkFunction("insert into dws_traffic_keyword_page_view_window values(?,?,?,?,?)")
        );
        env.execute();
    }
}
